Consistency of Heckman-type two-step estimators for the multivariate sample-selection model

This analysis shows that multivariate generalizations to the classical Heckman (1976 Heckman, JJ. 1976. The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economics and Social Measurement, 5: 475–92.  [Google Scholar], 1979) two-step estimator that account for cross-equation correlation and use the inverse Mills ratio as correction term are consistent only if certain restrictions apply to the true error-covariance structure. An alternative class of generalizations to the classical Heckman two-step approach is derived that condition on the entire selection pattern rather than selection in particular equations and, therefore, use modified correction terms. It is shown that this class of estimators is consistent. In addition, Monte-Carlo results illustrate that these estimators display a smaller mean square prediction error.     

When outcome heterogeneously matters for selection: a generalized selection correction estimator

The classical Heckman (1976, 1979) selection correction estimator (heckit) is misspecified and inconsistent, if an interaction of the outcome variable with an explanatory variable matters for selection. To address this specification problem, a full information maximum likelihood (FIML) estimator and a simple two-step estimator are developed. Monte Carlo (MC) simulations illustrate that the bias of the ordinary heckit estimator is removed by these generalized estimation procedures. Along with OLS and ordinary heckit, we apply these estimators to data from a randomized trial that evaluates the effectiveness of financial incentives for reducing obesity. Estimation results indicate that the choice of the estimation procedure clearly matters.     

Econometric models with normal polychotomous selectivity

The polychotomous extension of the selectivity model of Heckman (1976) is derived. Unlike similar models proposed by Hay (1980) and Lee (1983), this generalization maintains the assumption of joint normality among the errors. In light of recent advances in the theory and estimation of the multinomial probit model, the present derivation is offered as an important first step toward the true generalization of the Heckman estimator.     

Semiparametric Estimation of the Intercept of a Sample Selection Model

This paper provides a consistent and asymptotically normal estimator for the intercept of a semiparametrically estimated sample selection model. The estimator uses a decreasingly small fraction of all observations as the sample size goes to infinity, as in Heckman (1990). In the semiparametrics literature, estimation of the intercept has typically been subsumed in the nonparametric sample selection bias correction term. The estimation of the intercept, however, is important from an economic perspective. For instance, it permits one to determine the "wage gap" between unionized and nonunionized workers, decompose the wage differential between different socio-economic groups (e.g. male–female and black–white), and evaluate the net benefits of a social programme.     

Minimum income in a transition economy

The Albanian Ndihma Ekonomike is one of the first poverty reduction programmes launched in transitional economies. Its record has been judged positively during the recession period of the 1990s and negatively during the more recent growth phase. This article reconsiders the programme using a regression‐adjusted local linear matching estimator first suggested by Heckman et al. (1997, 1998). We find the programme to have a weak targeting capacity and a non‐significant impact on different household outcomes.     

Deriving conditional and unconditional marginal effects in log earnings equations estimated by Heckman's procedure

Although the Heckman approach has often been used in empirical analysis, the marginal effects, necessary to interpret the effect of the regressors on the dependent variable, appeared to be overlooked. Using the Heckman approach, general expressions are derived for calculating the conditional and unconditional marginal effects. Based on a sample of Brazilian women, the conditional and unconditional return to education are calculated for the logarithm of earnings equation estimated by Heckman's procedure, comparing them to the marginal effect of education obtained without correcting for selectivity bias. The same analysis is carried out for a discrete variable ‘black’.     

Small Sample Properties Of Existing And New Estimators Of The Autocorrelation Coefficient And Regression Coefficients In The Context Of Ar(1) Errors

This study systematically and comprehensively investigates the small sample properties of the existing and some new estimators of the autocorrelation coefficient and of the regression coefficients in a linear regression model when errors follow an autoregressive process of order one. The new estimators of autocorrelation coefficient proposed here are based on the jackknife procedure. The jackknife procedure is applied in two alternative ways: first to the regression itself, and second to the residuals of the regression model. Next, the performance of the existing and new estimators of autocorrelation coefficient (thirty-three in total) is investigated in terms of bias and the root mean squared errors. Finally, we have systematically compared all of the estimators of the regression coefficients (again thirty-three) in terms of efficiency and their performance in hypothesis testing. We observe that the performance of the autocorrelation coefficient estimators is dependent upon the degree of autocorrelation and whether the autocorrelation is positive or negative. We do not observe a direct link between the bias and efficiency of an estimator. The performance of the estimators of the regression coefficients also depends upon the degree of autocorrelation. If the efficiency of regression estimator is of concern, then the iterative Prais-Winsten estimator should be used since it is most efficient for the widest range of independent variables and values of the autocorrelation coefficient. If testing of the hypothesis is of concern, then the estimators based on jackknife technique are certainly superior and are highly recommended. However, for negative values of the autocorrelation coefficient, the estimators based on Quenouille procedure and iterative Prais-Winsten estimator are comparable. But, for computational ease iterative Prais-Winsten estimator is recommended.

     

State Dependence in Welfare Benefits in a Non-Welfare Context

This study investigates state dependence in social assistance benefits in Turkey, where benefit receipt and persistence rates have significantly increased over the past decade. We estimate state dependence through dynamic random-effects probit models, controlling for observed and unobserved heterogeneity, and endogenous initial conditions. In particular, we employ Wooldridge’s estimator to achieve consistent and correct estimates of state dependence and compare the results with estimates from Heckman’s reduced-form approach as a sensitivity check. Both estimators enable us to disentangle true state dependence from its spurious components and address the potential bias due to the short panel length. Our results suggest that the receipt of benefits in the last year increases the likelihood of benefit receipt in the current year, namely the structural state dependence, by 17.2–19.5 percentage points.     

Breaking the cycle? The effect of education on welfare receipt among children of welfare recipients

We examine the impact of high school graduation on the probability individuals from welfare backgrounds use welfare themselves. Our data consist of administrative educational records for grade 12 students in a Canadian province linked with their own and their parents' welfare records. We address potential endogeneity problems by: 1) controlling for ability using past test scores; 2) using an instrument for graduation based on school principal fixed effects; and 3) using a Heckman–Singer type unobserved heterogeneity estimator. Graduation would reduce the probability of welfare receipt of drop-outs by 1/2 to 3/4. Effects are larger for individuals from troubled family backgrounds and low income neighbourhoods.     

战略性新兴产业政策依赖性探析 ——来自地方政府补贴视角的实证检验

战略性新兴产业发展受到各级政府普遍扶持,扶持过程处处存在政府的影响,是研究政府行为的良好载体。为此,本文以战略性新兴产业为研究对象,以海克曼（Heckman）两步选择模型为基础分析地方政府补贴选择以及补贴实施机制,并考察其区域效应、省际效应及具体地区经济特征的影响。本文研究表明,地方政府行为具有多样性,其具有显著的扶持弱者行为取向,这是政策依赖的重要因素,这一影响机制受地区经济发展因素的影响,存在显著的区域差异。研究还表明,地方政府补贴行为具有较强的所有制类型偏好,中央国有企业受到补贴的概率、补贴数量及补贴程度显著低于其他所有制,其中蕴含着地方政府与中央国有企业之间的诸多问题。     

Logistics performance and export competitiveness: European experience

Logistics performance has become a decisive factor in export competitiveness. At the same time, and as a result of the continuous enlargement processes it has undergone, the European Union (EU) is a very interesting case to study how the reforms that enhance logistics performance have affected exports. Therefore, this paper aims to analyse the importance of logistics performance in regard to EU exports over the period 2005–2010 in an attempt to identify possible advances on behalf of Member States. Several gravity equations are estimated using the Logistics Performance Index (LPI) and its components as characteristic proxy variables of trade facilitation. In order to avoid the possible heterogeneity caused by sample bias, the two-stage model proposed by Heckman is used. The estimations of the gravity models using the two-stage Heckman model for all 26 EU countries have led us to conclude that logistics was more important for exporting nations than importing nations in both 2005 and 2010, reinforcing the interest in the exporter side of the paper. In reference to the components of the LPI, Competence and Tracking has acquired greater importance in recent years, in keeping with the weak domestic demand in European countries and the search for new international markets.     

Estimation of Censored Regression Model: A Simulation Study

We investigate the finite sample performance of several estimators proposed for the panel data Tobit regression model with individual effects, including Honoré estimator, Hansen’s best two-step GMM estimator, the continuously updating GMM estimator, and the empirical likelihood estimator (ELE). The latter three estimators are based on more conditional moment restrictions than the Honoré estimator, and consequently are more efficient in large samples. Although the latter three estimators are asymptotically equivalent, the last two have better finite sample performance. However, our simulation reveals that the continuously updating GMM estimator performs no better, and in most cases is worse than Honoré estimator in small samples. The reason for this finding is that the latter three estimators are based on more moment restrictions that require discarding observations. In our designs, about seventy percent of observations are discarded. The insufficiently few number of observations leads to an imprecise weighted matrix estimate, which in turn leads to unreliable estimates. This study calls for an alternative estimation method that does not rely on trimming for finite sample panel data censored regression model.     

The relative efficiency of a robust generalized Bayes estimator in a linear regression model with multicollinearity

This paper uses Monte Carlo techniques to examine the performance of a robust generalized Bayes estimator for a linear regression model when multicollinearity is present. Unlike many improved estimators, this near-minimax estimator performs very well under squared error loss even when the data are ill-conditioned.     

Economic benefits of using realized covariance forecasts in risk-based portfolios

This article examines the economic benefit of using the realized covariance matrix forecasts, for constructing the risk-based portfolios. We use the two-scale realized covariance estimator (TSC), the jump robust two-scale realized covariance estimator (RTSC) and the realized bipower covariance estimator (BPC), to forecast the daily realized covariance matrix. Using these covariance matrix forecasts, we implement three risk-based portfolios: the global minimum variance portfolio, the equal risk contribution portfolio and the most diversified portfolio. There is evidence that the portfolio performance improves by using TSC or RTSC estimators as compared to the daily-returns-based estimator. The performance gains are robust to the choice of risk-based portfolio strategy, the degree of investor’s relative risk-aversion, the market conditions and the choice of time intervals.     

Estimation of a Tobit model with unknown censoring threshold

Conventional wisdom suggests that only the estimated intercept is affected by imposition of a zero censoring threshold on a Tobit model. This is true for Heckman-Lee estimation. For maximum likelihood (ML) estimation, however, it is only true if the censoring threshold is known and is subtracted from the dependent variable. Failure to properly transform the dependent variable prior to ML estimation of a zero threshold Tobit model will generally bias the coefficient estimates. A long neglected topic is ML estimation of a Tobit model with common, but unknown, censoring threshold. This paper shows that the ML estimator of the censoring threshold is the minimum order statistic from the observed subsample, and that existing software for estimation of a zero-threshold Tobit model is easily adapted to include estimation of the censoring threshold.     

Estimation of Discrete Choice Dynamic Programming Models

This study reviews estimation methods for the infinite horizon discrete choice dynamic programming models and conducts Monte Carlo experiments. We consider: the maximum likelihood estimator (MLE), the two‐step conditional choice probabilities estimator, sequential estimators based on policy iterations mapping under finite dependence, and sequential estimators based on value iteration mappings. Our simulation result shows that the estimation performance of the sequential estimators based on policy iterations and value iteration mappings is largely comparable to the MLE, while they achieve substantial computation gains over the MLE by a factor of 100 for a model with a moderately large state space.     

Estimating the variance of decomposition effects

We derive the asymptotic variance of the Blinder–Oaxaca decomposition effects. We show that the delta method approach that builds on the assumption of fixed regressors understates true variability of the decomposition effects when regressors are stochastic. Our proposed variance estimator takes randomness of regressors into consideration. Our approach is applicable to both the linear and nonlinear decompositions. Previously, only a bootstrap method has been a valid option for nonlinear decompositions. As our derivation follows the general framework of m-estimation, it is straightforward to extend our variance estimator to a cluster-robust variance estimator. We demonstrate the finite-sample performance of our variance estimator with a Monte Carlo study and present a real-data application.     

The philosophical contributions of Ludwig von Mises

This paper was prepared for consideration for a Ludwig von Mises prize that was awared at the Mises Institute's 10th anniversary conference, October 9–11, 1992. I am grateful to Pat Heckman and Ralph Raico for their very helpful suggestions.     

Corporation Diversification and Firm Performance: Evidence from Vietnamese Listed Firms

We examine the effect of corporate diversification on the performance of firms listed on the Vietnamese stock exchanges, using 2744 firm year observations over the period from 2007 to 2012. We find that corporate diversification has a negative impact on firm performance. Our results are robust to various econometric estimation techniques including fixed effect, instrumental fixed effect, Heckman selection model and system generalised method of moments. In the Vietnamese context, the lack of an efficient corporate governance system may encourage firms to follow corporate diversification strategies, thus impairing their performance.